import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowerCamelCase__  =					False




class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 pass




@nightly
@require_torch_gpu
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Optional[int]:


   """simple docstring"""







   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	int:


   """simple docstring"""







   _UpperCamelCase           =    VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion")
   # remove text_unet
   pipe.remove_unused_weights()
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    "A painting of a squirrel eating a burger "
   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    pipe(
       prompt=lowercase_							, generator=lowercase_							, guidance_scale=7.5							, num_inference_steps=2							, output_type="numpy").images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(lowercase_)
     _UpperCamelCase           =    VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase_)
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    generator.manual_seed(0)
   _UpperCamelCase           =    pipe(
       prompt=lowercase_							, generator=lowercase_							, guidance_scale=7.5							, num_inference_steps=2							, output_type="numpy").images

   assert np.abs(image - new_image).sum() < 1e-5, "Models don't have the same forward pass"







 def 		__UpperCAmelCase				(		self				:   str)						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    VersatileDiffusionTextToImagePipeline.from_pretrained(
       "shi-labs/versatile-diffusion"							, torch_dtype=torch.floataa)
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    "A painting of a squirrel eating a burger "
   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    pipe(
       prompt=lowercase_							, generator=lowercase_							, guidance_scale=7.5							, num_inference_steps=50							, output_type="numpy").images

   _UpperCamelCase           =    image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _UpperCamelCase           =    np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2





from __future__ import annotations

from collections import Counter
from random import random




class 					_UpperCAmelCase			:



 '''simple docstring'''




 def __init__(		self				:   List[str])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   str)						->	None:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   str							, lowercase_				:   str							, lowercase_				:   float)						->	None:


   """simple docstring"""







   if nodea not in self.connections:
     self.add_node(lowercase_)
   if nodea not in self.connections:
     self.add_node(lowercase_)
   _UpperCamelCase           =    probability







 def 		__UpperCAmelCase				(		self				:   Any)						->	list[str]:


   """simple docstring"""







   return list(self.connections)







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   str)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    0
   _UpperCamelCase           =    random()

   for dest in self.connections[node]:
     current_probability += self.connections[node][dest]
     if current_probability > random_value:
       return dest
   return ""


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->dict[str, int]:




  '''simple docstring'''

  _UpperCamelCase           =    MarkovChainGraphUndirectedUnweighted()

  for nodea, nodea, probability in transitions:
    graph.add_transition_probability(a__    ,     a__    ,     a__ )

  _UpperCamelCase           =    Counter(graph.get_nodes() )
  _UpperCamelCase           =    start

  for _ in range(a__ ):
    _UpperCamelCase           =    graph.transition(a__ )
    visited[node] += 1

  return visited


if __name__ == "__main__":
   import doctest

   doctest.testmod()




